Cross-Modal and Contrastive Optimization for Explainable Multimodal Recognition of Predatory and Parasitic Insects

Natural enemies play a vital role in pest suppression and ecological balance within agricultural ecosystems. However, conventional vision-based recognition methods are highly susceptible to illumination variation, occlusion, and background noise in complex field environments, making it difficult to accurately distinguish morphologically similar species. To address these challenges, a multimodal natural enemy recognition and ecological interpretation framework, termed MAVC-XAI, is proposed to enhance recognition accuracy and ecological interpretability in real-world agricultural scenarios. The framework employs a dual-branch spatiotemporal feature extraction network for deep modeling of both visual and acoustic signals, introduces a cross-modal sampling attention mechanism for dynamic inter-modality alignment, and incorporates cross-species contrastive learning to optimize inter-class feature boundaries. Additionally, an explainable generation module is designed to provide ecological visualizations of the model's decision-making process in both visual and acoustic domains. Experiments conducted on multimodal datasets collected across multiple agricultural regions confirm the effectiveness of the proposed approach. The MAVC-XAI framework achieves an accuracy of 0.938, a precision of 0.932, a recall of 0.927, an F1-score of 0.929, an mAP@50 of 0.872, and a Top-5 recognition rate of 97.8%, all significantly surpassing unimodal models such as ResNet, Swin-T, and VGGish, as well as multimodal baselines including MMBT and ViLT. Ablation experiments further validate the critical contributions of the cross-modal sampling attention and contrastive learning modules to performance enhancement. The proposed framework not only enables high-precision natural enemy identification under complex ecological conditions but also provides an interpretable and intelligent foundation for AI-driven ecological pest management and food security monitoring.